Senior Data Engineer: ML & Cloud Data Platform

iHerb; United States; Hybrid; USD 116,000 - 170,000; Full time

Job summary

iHerb is seeking a Senior Data Engineer to evolve and scale our modern data ecosystem, including data lake, data warehouse, and ML-enable platforms. You will collaborate with data scientists and analytics engineers to deliver reliable, high‑quality data and production‑grade ML solutions.

Responsibilities include building scalable data pipelines, ensuring governance and observability, and deploying ML models with robust MLOps practices.

Qualifications

• Bachelor or Master’s degree in a technical field such as Computer Science or Information Systems.

Responsibilities

• Designs and builds scalable data extracts, integrations, transformations, and data models.
• Ensures successful deployment and provisioning of data solutions across environments.
• Designs data architectures enabling speed, quality, and operational efficiency.
• Interacts with stakeholders to gather requirements and translate into designs.
• Develops domain knowledge and data quality.
• Reviews requirements and drives resolution with stakeholders.
• Establishes MLOps standards and automation patterns for teams.
• Designs ground-up cloud data platform and pipelines.
• Partners with data scientists to build reproducible ML pipelines including feature engineering and monitoring.
• Implements CI/CD for data and ML workflows.

Skills

5+ years data engineering

Python

SQL

REST APIs

Data modeling

Agile teamwork

Data quality

Communication

Education

Bachelor's/Master's in CS/IT/related

Tools

Databricks

S3/Redshift/BigQuery

MLflow

SageMaker

Kubeflow

Docker

Job description

iHerb is seeking a Senior Data Engineer to evolve and scale our modern data ecosystem, including data lake, data warehouse, and ML-enable platforms. You will collaborate with data scientists and analytics engineers to deliver reliable, high‑quality data and production‑grade ML solutions.

Responsibilities include building scalable data pipelines, ensuring governance and observability, and deploying ML models with robust MLOps practices.